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Found 20 Skills
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
A disciplined diagnostic loop for tricky bugs and performance regressions. Reproduce → Minimize → Hypothesize → Instrument → Fix → Regression-test. Use this when the user says "diagnose this" / "debug this", reports a bug, states that something is broken/throwing errors/failing, or describes a performance regression.
Use when working with error diagnostics smart debug
This skill should be used when the user asks to "investigate an issue", "debug a problem", "find out why something is slow", "check error rates", "analyze user behavior", "understand a production incident", "query telemetry data", "look at logs", "check traces", "examine spans", "analyze RUM data", "check frontend performance", "investigate backend latency", "find transaction data", "check payment metrics", "analyze user journeys", or wants to answer questions using observability data from logs, metrics, traces, RUM, or APM - this is the gateway skill for deciding where to look first.
Search Sourcegraph-indexed codebases for patterns, examples, and system understanding. Triggers on implementation questions, debugging, or "how does X work" queries.
This skill should be used when encountering bugs, errors, failing tests, or unexpected behavior. Provides systematic debugging with evidence-based root cause investigation using a four-stage framework.
Systematic root-cause debugging: reproduce, investigate, hypothesize, fix with verification. Use when asked to "debug this", "fix this bug", "why is this failing", "troubleshoot", or mentions errors, stack traces, broken tests, flaky tests, regressions, or unexpected behavior.
This skill should be used when user asks about "GCloud logs", "Cloud Logging queries", "Google Cloud metrics", "GCP observability", "trace analysis", or "debugging production issues on GCP".
Scientific method expert for systematic bug investigation and root cause analysis. Use when users report bugs, crashes, unexpected behavior, or debugging requests. Applies hypothesis-driven investigation, controlled experiments, and rigorous validation across any programming language or platform.
Executes browser workflows while monitoring JavaScript console for errors, warnings, and messages. Use when you need error-free validation, debugging context during automation, or proactive error detection in web applications. Triggers on "check for console errors", "monitor JavaScript errors", "validate error-free execution", or "debug browser workflow". Works with Playwright MCP browser automation tools.
The orchestrator and entry point for the engineering skills suite. Use this skill whenever the task involves doing engineering work to a high bar — reviewing code or a design, designing a new system or component, debugging a hard problem or running an incident, implementing a substantive change, writing documentation, or sanity-checking an approach. Use it when the user phrases things casually ("rip into this", "be brutal", "is this approach right", "what am I missing", "what would you change", "look at this") or formally ("review this PR", "audit this design"). Use it proactively for any non-trivial engineering work, before declaring something done. The skill triages the work, dispatches to the right specialty skill(s), enforces verification, and produces an evidence-backed result. The goal is to ensure no AI shortcut, sycophantic agreement, or stylistic distraction gets in the way of work that holds up to senior-engineer scrutiny.